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Mu-Yen Chen; Hsin-Te Wu. Real-time intelligent image processing for the internet of things. Journal of Real-Time Image Processing 2021, 1 -2.
AMA StyleMu-Yen Chen, Hsin-Te Wu. Real-time intelligent image processing for the internet of things. Journal of Real-Time Image Processing. 2021; ():1-2.
Chicago/Turabian StyleMu-Yen Chen; Hsin-Te Wu. 2021. "Real-time intelligent image processing for the internet of things." Journal of Real-Time Image Processing , no. : 1-2.
Hsin-Te Wu. The internet-of-vehicle traffic condition system developed by artificial intelligence of things. The Journal of Supercomputing 2021, 1 .
AMA StyleHsin-Te Wu. The internet-of-vehicle traffic condition system developed by artificial intelligence of things. The Journal of Supercomputing. 2021; ():1.
Chicago/Turabian StyleHsin-Te Wu. 2021. "The internet-of-vehicle traffic condition system developed by artificial intelligence of things." The Journal of Supercomputing , no. : 1.
Today, the agriculture industry has been developing intellectualization and automation proactively for reducing labor force and increase yields. In the past, farmers usually followed the rule of thumb to grow crops; however, due to the dramatic climate change, it becomes harder for farmers to cope with it by merely following the rule of thumb, which leads to crop damage. Therefore, it is vital to input scientific data development and technology for optimizing the environment parameters of crops and further enhance the yields. Additionally, many farms need to spread pesticides to avoid pests and diseases; yet, too much pesticide may cause soil alkalization. To enrich the growing-power of the lands, farmers will fertilize the lands; nevertheless, too much of it will also cause soil acidification, which will need to leave the land fallow to improve the soil quality. The study provides an intelligent agriculture system based on LSTM. The system develops an Internet of Things (IoT) to monitor the environmental conditions of soil, sunlight, and temperature; additionally, the research combines the information from the Central Weather Bureau for predicting the timing for watering and notifying farmers about the suggested amount of pesticides and fertilizers. The features of this article are as follows: 1. Build a clustering tree of crops according to the adaptability; 2. Calculate the critical values of each selected crop; 3. Develop an LSTM system that provides analyses according to the current soil conditions and weather forecast information; the system will reveal the conditions of the soil, and water the land to balance the condition and reach an optimal status if the soil pH is too high. The research is capable of enhancing crop yields and optimizing the land.
Hsin-Te Wu. Developing an Intelligent Agricultural System Based on Long Short-Term Memory. Mobile Networks and Applications 2021, 26, 1397 -1406.
AMA StyleHsin-Te Wu. Developing an Intelligent Agricultural System Based on Long Short-Term Memory. Mobile Networks and Applications. 2021; 26 (3):1397-1406.
Chicago/Turabian StyleHsin-Te Wu. 2021. "Developing an Intelligent Agricultural System Based on Long Short-Term Memory." Mobile Networks and Applications 26, no. 3: 1397-1406.
The sustainable utilization of marine resources is a vital issue to enrich marine life and to prevent species extinction caused by overfishing. Nowadays, it is common that commercial and smaller vessels are equipped with an Automatic Identification System (AIS) and GPS for better vessel tracking to avoid vessel collision as well as mayday calls. Additionally, governments can monitor vessels’ sea activities through AIS messages, stopping them from overfishing or tracking if any vessel has caused marine pollution. However, because AIS devices cannot guarantee data security, they are susceptible to malicious attacks such as message modification or an illegitimate identity faking a distress signal that causes other vessels to change their course. Given the above, a comprehensive network security system of a sustainable marine environment should be proposed to ensure secure communication. In this paper, a stationary IoT-enabled (Internet of Things) vessel tracking system of a sustainable marine environment is proposed. The system combines network security, edge computing, and tracking management. It offers the following functions: (1) The IoT-based vessel tracking system tracks each aquafarmer’s farming zone and issues periodic warning to prevent vessel collision for pursuing a sustainable marine environment; (2) the system can serve as a relay station that evaluates whether a vessel’s AIS data is correct; (3) the system detects abnormal behavior and any irregular information to law enforcement; (4) the system’s network security mechanism adopts a group key approach to ensure secure communication between vessels; and (5) the proposed edge computing mechanism enables the tracking system to perform message authentication and analysis, and to reduce computational burden for the remote or cloud server. Experiment results indicate that our proposed system is feasible, secure, and sustainable for the marine environment, and the tendered network security mechanism can reduce the computational burden while still ensuring security.
Han-Chieh Chao; Hsin-Te Wu; Fan-Hsun Tseng. AIS Meets IoT: A Network Security Mechanism of Sustainable Marine Resource Based on Edge Computing. Sustainability 2021, 13, 3048 .
AMA StyleHan-Chieh Chao, Hsin-Te Wu, Fan-Hsun Tseng. AIS Meets IoT: A Network Security Mechanism of Sustainable Marine Resource Based on Edge Computing. Sustainability. 2021; 13 (6):3048.
Chicago/Turabian StyleHan-Chieh Chao; Hsin-Te Wu; Fan-Hsun Tseng. 2021. "AIS Meets IoT: A Network Security Mechanism of Sustainable Marine Resource Based on Edge Computing." Sustainability 13, no. 6: 3048.
In order to maintain the data privacy and security for consumers, the data encryption is closely related to our daily lives. The symmetric encryption is currently the most widely used encryption method in consumer electronics fields, especially in services involving Internet of Thing devices due to easy usage, quickly and lower computing costs. However, most of symmetric encryptions adopt quite linear key generation scheme so that the generated keys still have higher opportunity to be cracked. In this paper, we define a model to identify the generated key's randomness and further propose a novel metaheuristic-based key generation framework to improve the symmetric encryption drawbacks.
Hsin-Hung Cho; Min-Yan Tsai; Fan-Hsun Tseng; Hsin-Te Wu; Chi-Yuan Chen. Improving Randomness of Symmetric Encryption for Consumer Privacy using Metaheuristic-based Framework. IEEE Consumer Electronics Magazine 2021, PP, 1 -1.
AMA StyleHsin-Hung Cho, Min-Yan Tsai, Fan-Hsun Tseng, Hsin-Te Wu, Chi-Yuan Chen. Improving Randomness of Symmetric Encryption for Consumer Privacy using Metaheuristic-based Framework. IEEE Consumer Electronics Magazine. 2021; PP (99):1-1.
Chicago/Turabian StyleHsin-Hung Cho; Min-Yan Tsai; Fan-Hsun Tseng; Hsin-Te Wu; Chi-Yuan Chen. 2021. "Improving Randomness of Symmetric Encryption for Consumer Privacy using Metaheuristic-based Framework." IEEE Consumer Electronics Magazine PP, no. 99: 1-1.
Intelligent industrial production has recently emerged as an important trend for application of the Industrial Internet of Things (IIoT) in edge computing. This study applied remote edge devices and edge servers, preprocessing the signal sensor, through covert data to cloud storage, and loaded the data to propose several deep learning methods to assess the status of aircraft engines in operation, and to classify stages of operational degradation so as to predict the functional remaining lifespan of components. The predicted results are transmitted to a cloud-based server for monitoring and maintenance.
Hsin-Yao Hsu; Gautam Srivastava; Hsin-Te Wu; Mu-Yen Chen. Remaining useful life prediction based on state assessment using edge computing on deep learning. Computer Communications 2020, 160, 91 -100.
AMA StyleHsin-Yao Hsu, Gautam Srivastava, Hsin-Te Wu, Mu-Yen Chen. Remaining useful life prediction based on state assessment using edge computing on deep learning. Computer Communications. 2020; 160 ():91-100.
Chicago/Turabian StyleHsin-Yao Hsu; Gautam Srivastava; Hsin-Te Wu; Mu-Yen Chen. 2020. "Remaining useful life prediction based on state assessment using edge computing on deep learning." Computer Communications 160, no. : 91-100.
Shrimp is a world’s important trade goods with high economic value and also one of the most important sources of animal protein. Considering the costs of calculation and hardware, this paper presents a convolutional neural network (CNN) architecture (named as ShrimpNet) to obtain shrimp recognition. The proposed ShrimpNet is an important part of the intelligent shrimp aquaculture which is great helpful for the shrimp aquaculture. The proposed ShrimpNet includes two CNN layers and two fully-connected layers. The collected data set includes six different categories of shrimp that are used to train and test the performance of proposed ShrimpNet. Experimental results show that the proposed ShrimpNet has 95.48% accuracy in shrimp recognition. Therefore, the proposed ShrimpNet is a useful tool with good performance for shrimp recognition.
Wu-Chih Hu; Hsin-Te Wu; Yi-Fan Zhang; Shu-Huan Zhang; Chun-Hung Lo. Shrimp recognition using ShrimpNet based on convolutional neural network. Journal of Ambient Intelligence and Humanized Computing 2020, 1 -8.
AMA StyleWu-Chih Hu, Hsin-Te Wu, Yi-Fan Zhang, Shu-Huan Zhang, Chun-Hung Lo. Shrimp recognition using ShrimpNet based on convolutional neural network. Journal of Ambient Intelligence and Humanized Computing. 2020; ():1-8.
Chicago/Turabian StyleWu-Chih Hu; Hsin-Te Wu; Yi-Fan Zhang; Shu-Huan Zhang; Chun-Hung Lo. 2020. "Shrimp recognition using ShrimpNet based on convolutional neural network." Journal of Ambient Intelligence and Humanized Computing , no. : 1-8.
With the growth of the human population comes the constantly rising demand for agricultural products. Nevertheless, as the world experiences climate change, many crops are often damaged by weather conditions.This study utilizes Intelligent Agriculture IoT equipment to monitor the environmental factors on a farm. The collected data underwent 3D cluster analysis to yield analysis of the environmental factors of that farm. The proposed scheme bears the following features: (1) data normalization is achieved via the combination of moving average and average variance; (2) we applied 3D cluster analysis to analyze the relation between environmental factors and subsequently examine the rules of thumb held by the farmers; (3) the system determines whether a selected crop has been placed in the appropriate cluster; and (4) the system sets a critical value in the cluster based on future environments and provides advice on whether a crop is suitable for the farm. We placed Intelligent Agriculture IoT equipment in the farm for monitoring purposes and ran an actual-scenario analysis using the algorithm in our study; results confirm that our proposed scheme is indeed feasible.
Fan-Hsun Tseng; Hsin-Hung Cho; Hsin-Te Wu. Applying Big Data for Intelligent Agriculture-Based Crop Selection Analysis. IEEE Access 2019, 7, 116965 -116974.
AMA StyleFan-Hsun Tseng, Hsin-Hung Cho, Hsin-Te Wu. Applying Big Data for Intelligent Agriculture-Based Crop Selection Analysis. IEEE Access. 2019; 7 ():116965-116974.
Chicago/Turabian StyleFan-Hsun Tseng; Hsin-Hung Cho; Hsin-Te Wu. 2019. "Applying Big Data for Intelligent Agriculture-Based Crop Selection Analysis." IEEE Access 7, no. : 116965-116974.
The Internet of Vehicle (IoV) utilizes networks to conduct message exchange and related services or application. In recent years, smart cities and IoVs have become areas of interest in the new generation Internet of Things development, especially since the development of intelligent transportation system has focused on bettering traffic conditions. This paper proposes establishing an intelligent transportation system with a network security mechanism in an IoV environment, with emphasis on the following aspects: 1) this paper integrates intelligent transportation systems in traffic signal control to aid emergency vehicles in more promptly arriving at its destination; 2) in the case of traffic incidents, this paper's approach allows regular vehicles to obtain proof of incident from pertaining authorities and learn about nearby vehicles global positioning system information, such as position and speed, and utilize their car camcorder data for proving purposes; and 3) this paper combines roadside units (RSUs) with traffic signal control and transmits important information to the certificate authority (CA) for storage. Given that RSUs are limited in computation ability and storage space, we can assess and filter the information before sending it to the CA, reducing RSUs computational burden and storage space usage. This paper satisfies IoVs network security requirements of authentication, non-repudiation, conditional anonymity, and conditional untraceability, and, as seen from experiment results, the proposed method is superior to that of other studies.
Hsin-Te Wu; Gwo-Jiun Horng. Establishing an Intelligent Transportation System With a Network Security Mechanism in an Internet of Vehicle Environment. IEEE Access 2017, 5, 19239 -19247.
AMA StyleHsin-Te Wu, Gwo-Jiun Horng. Establishing an Intelligent Transportation System With a Network Security Mechanism in an Internet of Vehicle Environment. IEEE Access. 2017; 5 ():19239-19247.
Chicago/Turabian StyleHsin-Te Wu; Gwo-Jiun Horng. 2017. "Establishing an Intelligent Transportation System With a Network Security Mechanism in an Internet of Vehicle Environment." IEEE Access 5, no. : 19239-19247.